A neural network based MRAC scheme with application to an autonomous nonlinear rotorcraft in the presence of input saturation. (September 2021)

• Record Type: Journal Article
• Authors: Wang, Yu; Li, Aijun; Yang, Shu; Li, Qiang; Ma, Zhao
• Is Part Of: ISA transactions. Volume 115(2021)
• Page Start: 1
• Page End: 11
• Publication Date: 2021-09
• Subjects: Model reference adaptive control -- Input saturation -- Neural network control -- Nonlinear rotorcraft model -- Projection operator; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.01.003
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper develops a neural-network-based model reference adaptive control (MRAC) scheme for a rotorcraft in the presence of input saturation. Such a control scheme provides acceptable tracking performance for the rotorcraft in a wide range of flight conditions. Combined with hyperbolic tangent functions, the MRAC scheme is capable of tracking the reference signals without violating input constraints. A modified projection operator is utilized to prevent system from parameter drift due to the strong nonlinearity and uncertainty of the rotorcraft mathematical model. Stability of the proposed MRAC scheme is proved based on Lyapunov stability theory. The performance of the resulting controller is tested by conducting numerical simulations for an autonomous rotorcraft in various flight missions. Highlights: A robust modification of adaptive control is proposed to prevent nonlinear system from parameter drift. A neural network based MRAC scheme is conducted in the presence of input saturation using hyperbolic tangent function. The proposed MRAC scheme is applied to a nonlinear rotorcraft model.